Speaker: Michael Lee, Professor of Cognitive Sciences, University of California Irvine
Room: NSH 1507
The analysis of crowdsourced data can be treated a cognitive modeling problem, with the goal of accounting for how any why people produced the behaviors that were observed. We explore this cognitive approach in a series of examples, involving Thurstonian models of ranking, calibration models of probability estimation, and attention and similarity models of category learning. Many of the demonstrations use crowd-sourced data from ranker.com. Some involve “wisdom of the crowd” predictions, while others aim to describe and explain the structure of people’s opinions. Throughout the talk, we emphasize the tight interplay between theory and application, highlighting not just when existing cognitive theories and models can help address crowd-sourcing problems, but also when real-world applications demand solutions to new basic research challenges in the cognitive sciences.
Michael Lee is a Professor of Cognitive Sciences at the University of California Irvine. His research focuses on modeling cognitive processes, especially of decision making, and the Bayesian implementation, evaluation, and application of those models. He has published over 150 journal and conference papers, and is the co-author of the graduate textbook “Bayesian cognitive modeling: A practical course”. He is a former President of the Society for Mathematical Psychology, a winner of the William K. Estes award of that society, and a winner of the best applied paper from the Cognitive Science Society. Before moving the U.S., he worked as a senior research scientist for the Australian Defence Science and Technology Organization, and has consulted for the Australian and US DoD, as well as various universities and companies, including the crowd-sourcing platform Ranker.